Data Science Success Stories from Science and Engineering - rmcgranaghan/data_science_tools_and_resources GitHub Wiki

Data Science Success Stories from Science and Engineering

This page contains a running list of 'home runs' (i.e., examples of success stories) in which data science has been applied in the sciences and engineering (and knowledge/progress was created that would not have been possible without data science). If you would like to contribute examples to this page, send me a request to be a collaborator! These home runs are particularly effective in helping change the culture and increase the adoption of these important methods. This is meant to be a resource for everyone to utilize to better communicate and creat change.

The Characteristics of a home run are:

  • They are extensible and have been extended
    • Create open data/software/hardware that are usable
  • They create a new community that is sustained beyond project lifetime
  • They advance the state-of-the-art
  • They push the limit of the intersection of physics/prior understanding and machine learning

What this page is NOT: a list of references to published papers. All home runs need speak to how they address the criteria above.

In the near-Earth space environment

  • Prediction of ionospheric scintillation
    • McGranaghan et al., [2018]: data wrangled large volume of ionospheric scintillation observations, aligned with solar wind and geomagnetic activity data to develop a predictive algorithm that advanced the state-of-the-art (measured by evaluative metric that can serve as a foundation on which future efforts can build)
    • Two consecutive NASA Frontier Development Laboratory teams have advanced this research
      • Team #1: Neural Networks + incorporation of ground-based magnetometer measurements
      • Team #2: Advanced convolutional neural networks + incorporation of auroral imagery data
  • Defense Meteorological Satellite Program Magnetometer (SSM) instrument, data products, discovery, and open source
    • Relevant reference: Kilcommons et al.; 2018
    • Method to reprocess DMSP SSM data with greater accuracy, curated into usable database with `added-value' products, and from which frontier scientific discovery is demonstrated
    • Work can be used and extended by the community by contributing all data products to the NASA CDAWeb Virtual Observatory and making the source code completely open
    • The data products now available are essential to organize space weather activity and are often needed in all studies, but seldom available prior to this work without considerable effort.
  • The Magnetospheric State Query System
    • Relevant reference: Fung and Shao; 2008
    • 30 years of solar wind and geomagnetic activity data used to show that magnetospheric state can be specified by a state vector
    • Work can be used and extended by the community by creating a digital resource (i.e., The Magnetospheric State Query System) based on the results and that provides access to the wealth of data used for the project
    • Magnetospheric state is critical to Heliophysics understanding and is a prerequisite to detailed specification of the near Earth space environment
  • SunPy
  • Magnetospheric multiscale mission (MMS) instrument completion
  • Aurorasaurus

Heliophysics and Planetary Science

Earth Science

Instrumentation and Engineering